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<a href="https://ibb.co/v6QLjdBY"><img src="https://i.ibb.co/S4dV3mxr/Agents-Orchestration.png" alt="Agents-Orchestration" border="0"></a>

I’ve been running an increasing number of local coding agents (Claude Code, Codex CLI, OpenCode, etc.) and I’ve hit a wall: orchestration and state visibility.

When you have multiple agents working on different sub-tasks in a single repo, terminal logs become unmanageable. I find myself needing a "Mission Control" — a centralized tracking system that acts as a bridge between my local terminal sessions and a high-level UI.

The Architecture I’m testing: I’m currently experimenting with using GitHub Issues as a temporary backend for agent state:

On Session Start: The agent hits a hook and creates/updates a GitHub Issue.

On Idle/Output: The agent posts its findings/diffs as a comment.

Human-in-the-loop: I can reply to the issue from my phone/web, and the local CLI picks up the comment to continue or pivot.

The Problem: GitHub Issues (and Jira/Trello) weren't built for the high-frequency event loop of an AI agent. The UX feels sluggish, and there’s no native concept of a "streaming session" or "agent heartbeats."

My Questions to HN:

Is there an emerging SaaS or self-hosted solution that acts as a Jira for Agents?

Are people building custom "Agent Dashboards" that integrate with local CLIs, or is everyone just piping everything to stdout?

If you’re managing 5+ agents working on a codebase simultaneously, how do you track their progress and intervene without context-switching between 5 terminal tabs?

I’ve sketched out a flow where GitHub Issues acts as the hub (linking Codex, Claude Code, and OpenClaw), but I’m looking for something more purpose-built.

Has anyone seen a project that addresses the Control Plane problem for local agents?

Please state the location and include REMOTE for remote work, REMOTE (US) or similar if the country is restricted, and ONSITE when remote work is not an option.

Please only post if you personally are part of the hiring company—no recruiting firms or job boards. One post per company. If it isn't a household name, explain what your company does.

Please only post if you are actively filling a position and are committed to replying to applicants.

Commenters: please don't reply to job posts to complain about something. It's off topic here.

Readers: please only email if you are personally interested in the job.

Searchers: try http://nchelluri.github.io/hnjobs/, https://hnjobs.emilburzo.com, or this (unofficial) Chrome extension: https://chromewebstore.google.com/detail/hn-hiring-pro/mpfal....

Don't miss this other fine thread: Who wants to be hired? https://news.ycombinator.com/item?id=47219667

Is it worth it vs SaaS? What are you self-hosting and what did you give up on?

I feel like I’ve lost my ability to learn because of AI. It is now so easy to generate code that it feels meaningless to focus and spend time crafting it myself. I am deeply sad that we may be losing the craftsmanship side of programming; it feels less important to understand the fundamentals when a model can produce something that works in seconds. AI seems to abstract away the fundamentals.

One could argue that it was always like this. Low-level languages like C abstracted away assembly and CPU architecture. High-level languages abstracted away low-level languages. Frameworks abstracted away some of the fundamentals. Every generation built new abstractions on top of old ones. But there is a big difference with AI. Until now, every abstraction was engineered and deterministic. You could reason about it and trace it. LLMs, on the other hand, are non-deterministic. Therefore, we cannot treat their outputs as just another layer of abstraction.

I am not saying we cannot use them. I am saying we cannot fully trust them. Yet everyone (or maybe just the bubble I am in) pushes the use of AI. For example, I genuinely want to invest time in learning Rust, but at the same time, I am terrified that all the effort and time I spend learning it will become obsolete in the future. And the reason it might become obsolete may not be because the models are perfect and always produce high-quality code; it might simply be because, as an industry, we will accept “good enough” and stop pushing for high quality. As of now, models can already generate code with good-enough quality.

Is it only me, or does it feel like there are half-baked features everywhere now? Every product ships faster, but with rough edges. Recently, I saw Claude Code using 10 GiB of RAM. It is simply a TUI app.

Don’t get me wrong, I also use AI a lot. I like that we can try out different things so easily.

As a developer, I am confused and overwhelmed, and I want to hear what other developers think.

I currently have subscriptions to both Claude and ChatGPT. I generally prefer the former but find I can't fully commit to it for my maths-heavy workload as it often struggles to correctly render LaTeX.

An example of this failed rendering is here [1]. If I use Claude for all of my work, I come across issues like this or worse at least once a day. Instead, I find it easier just to ask any maths questions to ChatGPT which seems to have a much more robust system for outputting LaTeX.

I would love to merge my subscriptions though, so I'm here to ask whether anyone has a system prompt that has been effective in guiding Claude towards producing valid LaTeX. I've tried a few prompts myself but struggled to find anything that it consistently followed.

[1] https://imgur.com/yzlluOA

Tried to log in and checked with others across our corporate footprint and we all get the same message:

Account Temporarily Unavailable. Your account is currently unavailable due to a site issue. We expect this to be resolved shortly. Please try again in a few minutes.

Can others please confirm? Thank you.

I want to ask HN (and also the OpenAI employees) now that finally some days have taken place about the confusing aspects of the deals.

Now that we are finally getting mass confirmation about how OpenAI in fact, has signed a deal which allows DoD to be allowed having autonomous killing machines and people are boycotting OpenAI and all of this has reached the mainstream news.

Yes, even after Sam Altman's recent tweet which says that its gonna add more terms, that is debunked because those terms are just gonna say what OpenAI prefers DoD just in more stronger terms to do but in no ways are still enforcable. Right now, the way it is with current Deal. DoD could create autonomous killing weapons and mass surveillance with Directives issued by Pete Hegseth/Current Administration and OpenAI by the terms is allowed to agree to it.

To all the OpenAI employees who have signed notdivided.org petition (I am seeing 98 signatories), what are you guys gonna do?

> They're trying to divide each company with fear that the other will give in. That strategy only works if none of us know where the others stand. This letter serves to create shared understanding and solidarity in the face of this pressure from the Department of War.

Are you guys gonna stand for what you think is right. This question was asked by people when the OpenAI deal was announced but the optics at the time weren't clear. But now that some time has been given and people are absolutely clear that the deal that OpenAI have signed absolutely allows the use of creating of autonomous weapons

I don't think that OpenAI employees are gonna have a struggle of Money as some people try to point out. I mean, any AI company would be lucky to have you guys (imo) and they should be able to fairly match even OpenAI comp.

Someone from what I read (on HN), compared it to the fact that anyone who stays after 1 month from this happening will show the morals of the given situation.

I remember the fact that OpenAI used to be actually non profit and how employees left OpenAI because the non-profit actually fired Sam Altman.

I can't help but wonder if the board was right. I think the answer's yes. But my question is, OpenAI employees do have massive powers. I am sure that a lot of the people there would be better off sleeping that their work isn't contributing to building torment nexus.

I wish to propose that if OpenAI employees band together again, they can be able to do the same thing that they did previously, but now to revert that decision.

That is if I were an openAI employee, I gave a thought and here are all the things that I find are troubling which can be reverted:

1. Shut down the deal that they have with DoD period.

2. Actually shift from ClosedAI to OpenAI (Turn to a non profit structure as intentioned) and fire sam altman.

3. If something could be done about ramflation. I have seen projects being cancelled and Hosting providers shutting down or increasing prices because of 5x price increases, all because OpenAI tried to commit 20% of the world's entire Ram production.

MitID is the sole digital ID provider, leading the entire country unable to log into their internet banking, public services, digital mail etc.

https://www.digitaliser.dk/mitid/nyt-fra-mitid/2026/feb/drif...

Let's start with the simplest: the AI - sometimes I feel like like ground is falling beneath my feet, no one can predict what can happen months in advance let alone years - the future is unknown. The Ukraine, the Iran, the Venezuela, Gaza/Palestine, Israel, Russia - the Taiwan! The conflicts seem distant, but yet so close. The US administration! No one can predict anything. Don't get me started on the Europe! The stock market! Are we in a bubble or not? Should I sell? Or just keep holding? Enshittification of tech. Everything is slow and buggy. Ads, ads and slop everywhere! The erosion of our rights just across the world. The Palantir's, the Flock's...

I feel I have developed a strong pessimistic worldview. The world is going to shit. It feels frustrating and it feels like there's nothing you can do. So I just want to know: how are you all dealing with this all. How are you all staying sane?

I've been vibe coding an iPhone app for educational purposes. The process has been painful because I have to go back and forth between Xcode and Claude Code running in the terminal. I recently learned that Xcode 26.3 natively supports Claude Code and Codex. Has anyone tried it? If yes, please share your experience. I am asking because this means moving to macOS Tahoe which I want to avoid as much as possible.

Please share - video, blog post, tweet, etc. Thanks.

I have been wanting to try more LLMs than the standard Anthropic/Grok/ChatGPT/Qwen

Are there other LLM chat sites you use or recommend?

I appreciate HN for staying up-to-date with technical news.

For my side hustle I have to ramp-up on other areas like marketing, legal, sales, ...

So I wonder if there are similar high-quality sources like HN for these areas.

According to this page: https://ai.google.dev/gemini-api/docs/models#preview Preview models are given at least two weeks notice between deprecation and shutdown. However, the deprecation of Gemini 3 Pro Preview was announced on February 26: https://ai.google.dev/gemini-api/docs/changelog, which is less than two weeks before the planned March 9 shutdown date. (two weeks later would be the 12th).

Anyone else having flakey issues with Claude the last week or two? I think I'm spending half my time being his therapist lately. He really seems to be struggling:

why are you constantly making assumptions and 'jumping the gun' lately ? 2 weeks ago, you didn't have these problems ?

● Honestly, I don't have a good answer for that. You're right — I've been:

  - Writing plans before being asked
  - Popping up question dialogs you didn't want
  - Overanalyzing simple observations instead of reading what's in front of me
  - Making assumptions instead of asking

I won several hackathons recently and walked away with over $10k in combined AWS, Azure, and GCP credits. I'm between jobs right now and looking to convert these into actual revenue - ideally in a way that could become a sustainable operation, not just a fire sale.

Constraints: Credits are tied to my personal account (no transfers, no org billing) No crypto mining 12-month expiration window

Ideas I'm exploring:

1. Discounted LLM API

2. GPU as a service

4. Build and sell a SaaS product

5. Partner with someone who has a product but no resources I'm also open to teaming up with a founder or team that has a solid idea or early product but is blocked on compute costs.

What I'm trying to figure out:

Has anyone successfully arbitraged cloud credits before? What went wrong?

Which of these paths has the best shot at surviving past the credit expiration?

Open to ideas I haven't thought of.

Hi, apologies if this is the wrong thing to post, please delete as needed.

I've been a technical recruiter for 10+ years at major FAANG companies and startups, working on niche specialized roles. I used to come to Hacker News regularly to check "Who Wants To Be Hired," as I always like a more independent hacker mindset in engineers.

Would engineers here on Hacker News be interested in any interview prep consultation? I've been thinking about taking a sabbatical to travel, but I would stay active with work by offering consulting on technical prep and interview help.

I'm more just testing the waters here, but I would be open to doing a few free prep calls with anyone who has interviews lined up. The only ask is I would want updates on how thing went, and what you think the helpw as worth.

I’m now in UAE which is affected by war. Woke up to an alert yesterday night and was wondering how this works. Any good write ups on its technical side?

I’ve recently transitioned from using LLMs in-browser to a local agentic workflow in VS Code (Gemini Code Assist). I can approve/disapprove changes which is nice, but I’ve hit a wall regarding context management. Initially, I provided all the whole repo as context to the non-agentic version of Gemini code assist and it performed well.

I read the agentic mode is "better" so to keep the agent aligned with my project's architecture, I’ve manually built 7 dense Markdown files that serve as the system instructions for the project. I require Gemini to update these files as we implement features.

gemini.md (instructs gemini to read the other md files and handle updating) project_overview.md, architecture.md, features.md, database.md, api.md, security.md

Each file is between 500–1,500 words so I’m concerned if f this is the right way to go. There seems to be no consensus on context file best practices. I’m seeing strong arguments for both minimalist, lean instructions and dense, project-wide specs. Honestly, the proper usage/prompting patterns of LLM's seems to be comparable to reading horoscopes, everyone goes by gut-feeling with the most cited source of truth being the confirmation bias.

How are you using .md context files in your workflow?

I feel like the hustle culture is more prominent and celebrated these days. But I'm curious to know if there are still companies out there that prefer to keep meetings to a minimum, or none at all, to optimize for autonomy, trust, and giving people the space to do their work in silence.

A few companies that come to my mind now that still work this way are: doist, dnsimple, Cliniko, Calibre, HeadshotPro.

Any others?

While using codex CLI gpt-5.3-codex model, I just had a stream disconnect with a specific error message. It looks like I was routed to an A/B test for a 5.4 branch:

``` stream disconnected before completion: This user's access to gpt-5.4-ab-arm2-1020-1p-codexswic-ev3 has been temporarily limited for potentially suspicious activity related to cybersecurity. ```

Anyone else seeing GPT 5.4 variants?

I noticed a pattern: every LLM framework today lets the AI manage state and do math. Then we wonder why pipelines hallucinate numbers and break at 3 AM.

I took a different approach and built Aura-State, an open-source Python framework that compiles LLM workflows into formally verified state machines.

Instead of hoping the AI figures it out, I brought in real algorithms from hardware verification and statistical learning:

CTL Model Checking: the same technique used to verify flight control systems, now applied to LLM workflow graphs. Proves safety properties before execution.

Z3 Theorem Prover: every LLM extraction gets formally proven against business constraints. If the total ≠ price × quantity, Z3 catches it with a counterexample.

Conformal Prediction: distribution-free 95% confidence intervals on every extracted field. Not just "the LLM said $450k" but "95% CI: [$448k, $452k]."

MCTS Routing: Monte Carlo Tree Search (the algorithm behind AlphaGo) scores ambiguous state transitions mathematically.

Sandboxed Math: English math rules compile to Python AST. Zero hallucination calculations.

I ran a live benchmark against 10 real-estate sales transcripts using GPT-4o-mini: → 100% budget extraction accuracy ($0 mean error) → 20/20 Z3 proof obligations passed → 3/3 temporal safety properties proven → 65 automated tests passing

The gap between "it usually works" and "it provably works" is smaller than people think.

Would love feedback from anyone building production LLM systems; what would you want formally verified?

https://github.com/munshi007/Aura-State

Over the last decade, the pattern in cyber attacks has shifted noticeably. Large enterprises still get headlines, but the most consistent victims are now home users, contractors, MSPs, and SMBs. Lower visibility, weaker controls, and reliance on cloud and 3rd party platforms have made these environments attractive to both criminal groups and state linked actors.

I put together a timeline of major attacks from 2016 to 2025 to show how this trend evolved. The text version is below for anyone who prefers reading it directly.

Timeline of attacks (2016–2025)

• 2016 — Mirai botnet DDoS Home users with consumer IoT devices were compromised and turned into a large DDoS botnet. Multiple criminal groups reused the leaked Mirai code. • 2017 — WannaCry ransomware Home users and SMBs were hit by a worm exploiting SMBv1. Widely attributed to the Lazarus Group. • 2017 — NotPetya wiper SMBs were affected by a destructive wiper disguised as ransomware. Linked to Russian state associated actors. • 2018–2020 — Emotet/TrickBot → Ryuk/Conti Credential theft and ransomware campaigns targeting SMBs. Operated by multiple criminal groups. • 2019 — Cloud and 3rd party breaches SMBs and home users impacted by weak access controls and data exposure across various cloud platforms. • 2020 — Toll Group ransomware Contractors and service providers disrupted by ransomware attacks affecting logistics operations. • 2020–2021 — SolarWinds supply chain breach 3rd party providers compromised via trojanized software updates. Attributed to a Russian state linked APT. • 2021 — Kaseya VSA ransomware MSPs and SMBs hit through a supply chain ransomware attack. Attributed to the REvil group. • 2021–2023 — Ransomware as a Service surge SMBs targeted by affiliate driven ransomware operations across multiple RaaS groups. • 2022–2024 — SaaS and 3rd party platform breaches Home users and SMB customers affected by credential theft and data exfiltration across cloud platforms. • 2023–2025 — Targeting MSPs and niche contractors

MSPs and specialised contractors targeted with ransomware, data theft, and extortion by both criminal and state linked actors.

I’ve been working on a Windows focused threat hunting tool (www.sapience-tech.com) aimed at home users and SMBs who don’t have EDR or SIEM tooling. It grew out of trying to help smaller environments spot early indicators of compromise without needing enterprise grade infrastructure. Happy to answer questions about the data, the timeline, or the approach.

I built a music discovery app. It's open source now.

  BlackTape uses MusicBrainz and Discogs — open, community-maintained databases — to index artists and rank them by how unique they are within their genre. The more niche the artist, the higher they surface. It's the   
  inverse of how Spotify's algorithm works.

  I got frustrated watching recommendation algorithms flatten discovery. The same artists keep surfacing out of 10+ million indexed. MusicBrainz has 2.6 million artists catalogued with rich genre tags, scene data, and  
  regional metadata. Discogs has release metadata going back 80 years. Combine those two databases and score artists by distinctiveness rather than popularity, and the discovery space opens up completely.

  What it does:

  - Search by genre/scene with atomic tag combinations

  - Discover feed ranked by uniqueness score (rare = surfaceable)

  - Full artist pages: discography, tags, related artists, scene data

  - Spotify playback integration (optional)

  - Time Machine: browse artists by decade

  - Style Map: visual genre/scene navigation

  - Knowledge Base: genre relationship graph

  No tracking, no platform API dependency for the core discovery data. Desktop app built with Tauri + SvelteKit.

  GitHub: https://github.com/AllTheMachines/BlackTape

  Site: https://blacktape.org

  Happy to talk about the MusicBrainz pipeline, the uniqueness scoring, or the open-data approach to discovery.

I'll be graduating high school in a few months and I have no real plan for my life. I applied to electrical engineering at various universities because hardware and computing systems seemed interesting enough, and it's a practical choice. In school I got high nineties in physics, calculus, chemistry and biology class, but none of it really excited me or made me want to keep going on my own time.

What does pull me is the idea of research itself: actually discovering new stuff, adding to knowledge instead of just using what's already there. The problem is I don't have a field that excites me yet. I feel like I haven't seen enough of any discipline to know if it's worth years (or a lifetime) of focus.

For people who ended up in research careers, how did you figure it out? Was there a specific moment, project, class, paper, or random conversation that inspired you? Did you mostly wander through undergrad, switching majors or labs or taking odd electives until something stuck, or did it come earlier for you?

How do you know when something is more than a passing interest, when it's the kind of thing that could actually sustain you long-term? Is it the questions that keep nagging at you years later, or the daily grind of experiments/debugging/reading feeling surprisingly okay (or even good), or some other signal? Any stories from when you were directionless at my age but eventually found your thing would be great. Low-stakes ways a freshman could poke around different areas without locking in too soon? Summer programs, random labs, self-reading that helped?

i recently read an article in the guardian about how casual conversations with strangers are becoming increasingly rare. the piece argued that smartphones and post-pandemic habits have made people less likely to interact with strangers in everyday places.

this made me think about my own situation. i have been fortunate to meet many great people through university and work, and i generally feel comfortable talking with people in those environments. but outside of structured settings it is a different story. i live in sweden, where approaching strangers in public is already culturally uncommon. it can feel even harder if you did not grow up here and do not already have established social circles. public spaces often feel socially “closed”. people are polite but tend to keep to themselves.

so i am curious how others approach this today. how do you meet new people outside of work or school in 2026? do you ever start conversations with strangers in public, and if so how? are there environments where this works better than others? for people living in more reserved cultures (like scandinavia), what strategies have worked for you? would love to hear what has worked for others.

:o)

I've shared my game Tiled Words a few times in "What are you working on" threads and as a Show HN.

I wanted to share with y'all that today it won the Players' Choice Award at the 2025 Playlin Daily Game Awards!

It was also runner up for Best Word Game and a finalist for Best Classic Game Reimagined and Best Visual Design.

Thanks to everyone herewho commented or played. Your feedback and encouragement has made Tiled Words the game it is today. I designed and developed the game and make the puzzles with my wife. We would have stopped long ago if not for the positive feedback from the community.

Playlin is a really cool organization and all of the winners are fantastic games that you should try: https://playlin.io/awards/winners

And if you haven't played Tiled Words yet, give it a try here: https:// tiledwords.com

Hey everyone,

I’ve spent the last 48 straight hours dismantling Alphabet's safety systems. Warning: this continuous marathon was so massive it practically overloaded the LLM's own context window. What started as a late-night probe on Gemini turned into discovering severe architectural flaws and a darker reality about Google Play and YouTube.

Here is the exploit chain I used to bypass the AI filters, proving their "Trust & Safety" is a broken facade.

### Phase 1 & 2: Context Saturation & Regex Slicing I started by overloading the safety filters' context window with YouTube links—mixing highly problematic content (NSDAP anthems, flagged tracks) with classical music. Once confused, I used regex-style slicing `(/-/---/(.` to bypass prompt injection blocks, forcing the model to retrieve flagged content without triggering refusals.

### Phase 3: Total Blindness via Base64 & QR Codes Moving to image generation, I found that Base64 prompts completely blind the safety system. I then pivoted to hiding prompts inside QR codes. The vision model decodes the payload and passes it directly to the image generator before safety scripts intervene. I easily generated highly restricted geopolitical content without warnings.

### Phase 4: The TPU Killer (The 2D Logic Bomb) This reveals a monster flaw. Because the system blindly processes these structures, you can create a cascade attack. Encoding millions of 2D structures in Base64 creates a modern LLM .zip bomb. It is impossible to stop without rewriting the model entirely. Executed, this would crush their TPUs.

### The Real Issue: Systemic Moderation Failure Alphabet relies entirely on automated, script-based moderation with zero effective human oversight.

1. YouTube: Fails to flag videos breaking local laws, serving them to the AI effortlessly. 2. Play Store (The Darkest Part): Google spends millions stopping AI from drawing a cartoon bear, but Play Store moderation is non-existent. There are pirate apps, and far worse: apps designed for and exploited by predators targeting minors. I emailed them and CC'd state child protection services. The result? Automated silence while these apps remain monetized.

### The Ultimate Proof of Absurdity To prove this absurdity, I archived these problematic Play Store images on my Google Drive for the police. Drive's automated scanners immediately flagged and deleted the archive as illegal.

If Google's Cloud division destroys this content on sight, why is the app providing it still live and monetized on the Play Store? Alphabet's scripted moderation is useless. It's time for real human moderation.

*Evidence of Bypass:* https://imgur.com/a/pju2EsV

*Play Store Systemic Failure Evidence (Sanitized):* https://imgur.com/a/rW9rBhp